Análisis de frecuencias de variables sociales

Complexity is becoming accepted as an inherent feature of the walking pattern (Rhea and Kiefer, 2016). Approaches to restoring “healthy” gait are being explored by using methods such as auditory fluctuating timing imperatives that entrain “healthy” complexities (Hunt et al., 2014, Marmelat et al., 2014). However, the effectiveness of such methods remains unclear. Furthermore, the function of complexity within the walking pattern is unknown, but it may relate to adaptability (Harbourne and Sterigou, 2011; van Emmerick et al., 2017). Therefore, the underlying goal of this study is to assess whether a paradigm for entraining complexity with consistency could be developed. Hence, the current study was conducted to assess the entrainment effect of

cueing to several auditory fluctuating timing imperatives and to determine the consistency and error of FSI entrainment.

The results of the study suggest that stride interval complexity can be prescribed in a specified direction based on the fractal characteristics of the fluctuating timing imperative, with consistency across three sessions. Also, the participants demonstrated error when matching stride interval complexity to the complexity of the timing imperative, which was dependent on the fractal characteristics of the timing imperative.

4.3.1. Group prescription and ceiling effect

The primary objective of this study was to examine the effectiveness of entraining stride interval complexity with the use of a fluctuating timing imperatives. The results demonstrated a significant change in stride interval complexity with the use of a fluctuating timing imperative. UN walking was consistent with previous literature, which demonstrated a fractality of ~ a = 0.75 (Hausdorff, 2007). The fluctuating timing imperatives (WN, PN, RN) elicited a deviation from observed UN fractality. This finding is consistent with previous literature, which has reported a prescription effect with the use of fluctuating timing imperatives (Hunt et al., 2014; Marmelat et al., 2014; Rhea et al., 2014).

Interestingly, on average, participants were unable to achieve a ³ 1.0 when entraining to the RN imperative, though the FSI of the RN timing imperative ranged between a = 1.31-1.34. Perhaps this result indicates a “ceiling” in the complexity observed in the healthy gait system. This ceiling is similar to findings presented in the literature, which have shown that participants have difficulty achieving a FSI above 1.0,

mechanism behind this effect is unknown, but may indicate that the gait system is not a true 1/f process (a = 1.0), and is instead composed of a mix of deterministic and random components in order to remain adaptable (Rhea and Kiefer, 2016; van Emmerick et al., 2017).

4.3.2. Consistency across sessions

A second objective of the study was to assess the consistency of the entrainment effect. This study was the first to assess whether complexity can be consistently entrained across multiple sessions. No main effect of SES (p = 0.069) was found, which suggests that WN, PN and RN demonstrated entrainment consistency across all three sessions. This finding is important for future investigations that aim to test gait control with the use of auditory fluctuating timing imperatives. Also, the results indicate that auditory fluctuating timing imperatives that differ in their fractal characteristics can prescribe gait complexity consistently for at least three sessions. Overall, it appears that the prescription effect of the auditory stimuli represents a reliable method for entraining gait complexity.

4.3.3. FSI entrainment error

In the current study, the absolute difference between the ISI complexity and IBI complexity was quantified to assess the error of entrainment. This was done to interpret the participants’ gait complexity relative to the auditory stimulus. Based on the collapsed results, it appears that the WN had the best results in terms of complexity entrainment error. However, this may be difficult to conclude, since as a combined group, the participants were 0.1 units away from the average fractality of the WN TI elicited in the

study. This effectively entrained a fractality that is out of the range of a = 0.50-0.59, and is no longer considered white noise.

In contrast, PN demonstrated that participants were considerably less accurate than with WN with entraining fractality of the TI (Table 4-2). Nonetheless the fractality remained within the bounds of pink noise (FSI = 0.6-1.5). RN was found to demonstrate the greatest error relative to the entrainment error of WN and PN timing imperatives. Perhaps the error with entrainment observed with PN and RN were due to the inability of participants to accurately time heel contact to the beat onset. However, this was out of the scope of the study, which was completed to assess the matching of FSI between stimulus and gait. Rhea (2014) did demonstrate that participants revealed a variety of strategies (e.g., reactive or proactive) when cueing a visual stimulus with FSI = 1.0, and participants were able to successfully match their gait complexity to the stimulus. Future investigations should assess the local performance (i.e., asynchrony between heel contact and beat onset) to discern whether the strategy of stepping relates to entrainment. Overall, it is difficult to assess, based on the FSI, the most effective TI in terms of entrainment error.

If error is assessed solely by the absolute difference between the stimulus complexity and gait complexity, then interestingly, the WN was best in terms of complexity entrainment. The authors believed that the PN would demonstrate the smallest error due to the similarity between PN fractality and the fractality observed in normal gait (Hausdorff, 2007). That hypothesis was based on the idea that information between two complex systems is maximal when the two systems have a similar

Previous literature has demonstrated that the effectiveness of auditory fluctuating timing imperative entrainment is greatest with a signal that is approximating a PN signal (Hunt et al., 2014). However, the methodology in our study was not entirely consistent with that of Hunt (2014), as that study infused a fractal stimulus approximating white noise, pink noise, and red noise into music, which was then used to entrain gait complexity. Furthermore, the length of the data trials in Hunt’s study were double (512 strides) that of the current study (255 strides). Of note, Marmelat (2014) showed results that were consistent with the current study’s findings, in that participants were able to match their gait complexity with that of a timing imperative approximating white noise. This may be due to similar methodologies, whereby the length of the auditory stimulus was 255 cued strides and was a simple beat (i.e., not infused into music).